Anomaly detection method of power purchase material data based on BIRCH clustering algorithm and time series
Ning Guo, pengju wang · 2023
At present, the conventional detection methods for abnormal data of power purchase materials mainly use correlation vector machines to extract and reduce the dimensions of data features. Due to less dimensions of data feature extraction, the detection effect is poor. In this regard, a method based on BIRCH clustering algorithm and time series for anomaly detection of power purchase material data is proposed. The data is preprocessed by removing outliers and supplementing missing values, and a time series autoregression model is constructed according to data dimensions to extract the flow characteristics of material data. The determination of abnormal data is realized by using local density threshold. In the experiment, the detection performance of the designed detection method is tested. The final results can prove that the proposed method has a low false detection rate and an ideal detection effect when it is used for abnormal detection of power purchase material data.